The present invention is directed to manufacturing process controllers, and in particular to a method and apparatus for continually driving a manufacturing process, based on monitored parameters, to produce an optimal product.
Many manufacturing processes currently include facilities for monitoring various parameters of the process and of the product produced by the process to regulate the quality of the product. To this end, sensors have been developed which provide indications of the various parameter values during the manufacturing process. In addition, information on the process can also be obtained by examining the products after they have been processed.
For example, consider a process in which one-inch wooden blocks are cut from larger blocks of wood. Monitored parameters may include an indication of the relative positions of the saw blades; the squareness of the block; the height width and depth of the block; the smoothness of the block surfaces; and the amount of power being used by the saws that cut the blocks.
In a continuously controlled process, a parameter such as block height may be monitored to determine when the process is out of tolerance. In some process control systems, the value of a parameter is modeled as a Gaussian distribution having a predetermined mean, M, and standard deviation, .sigma.. In one type of out-of-tolerance process control system, action to correct the process is only taken when the monitored parameters of a significant number of individual products have values that differ from the mean by more than three times the standard deviation (i.e..+-.3.sigma.).
This type of out-of-tolerence control system may not produce an optimal product when, for example, the degradation of a monitored process variable is gradual. In this instance, the value of the monitored variable deviates from the predetermined mean value slowly, so that a pattern of out-of-tolerance measurements may only be detected after a relatively large number of products which are out-of-tolerance have been produced. In addition, the products produced by the process, while inside the tolerance limits, may differ significantly from the desired mean parameter values. These products may be considered inferior to products which hew closely to the mean.
An exemplary out-of-tolerance control system is the C(p,k) system which is widely used in the industry. This system is described in a document by J. Hoskins et al. entitled "Statistical Process Control" Motorola Publication No. BR392/D which is hereby incorporated by reference for its teaching of the C(p,k) system. According to this system, each monitored variable of a process is modeled as a Gaussian distribution having mean and standard deviation values which define all acceptable products as being within plus and minus six standard deviations (.+-.6.sigma.) from the mean. Each process variable is statistically monitored. If the actual frequency distribution falls outside of the .+-.6.sigma. limits, the C(p,k) system alerts the operator that corrective action should be taken.
Simple out-of-tolerance control systems have several problems. First, the system cannot make adjustments based on a single item since there is no measured standard deviation to compare with an ideal standard deviation. Second, these systems do not provide any useful information to center the process if the range of the process (i.e. standard deviation) is zero. In this instance, all measured parameters would have a C(p,k) value of infinity except where the measured mean is exactly at tolerance; then, C(p,k) would have the undefined value of 0/0. Third, even when the target standard deviation is non-zero, the C(p,k) system does not provide information that can be used to reduce process range if the process mean is at the tolerance limit. In this instance, the C(p,k) values are zero since the process mean is within the tolerance limit.
In an out-of-tolerance system, if all of the previously produced products are included in the sample space that defines the actual distribution of process values, a slow degradation may not be recognized until a relatively large number of out-of-tolerance products have been produced. That is to say, as the process goes out of tolerance, the number of products which do not meet the tolerance criteria may be statistically insignificant relative to the total number of products that have been produced. In addition, rather than conforming to a Gaussian distribution having a predetermined mean and standard deviation, the parameter values of the actual products may have a mean value or a standard deviation that differs significantly from the target value.
Another problem that may be encountered when a out-of-tolerance control system is used, occurs when the target distribution for acceptable products is not symmetric. This may occur, for example, in a process which manufactures semiconductor resistors. Because resistivity is measured as ohms/square, the resistance is a function of the relative proportions of the resistor. Thus, if the width of a mask opening which defines a resistor may vary by .+-.50%, the impedance of the resistor may vary by -33% to +100%. In this instance, using a symmetric tolerance limit may result in control operations which are too stringent for resistors having increased impedance or too lax for resistors having decreased impedance.
U.S. Pat. No. 4,320,463 PRODUCTION CONTROL SYSTEM addressed these problems by defining the sample space as a moving window of parameter values. Since the number of samples in the sample space is reduced, a smaller number of out-of-tolerance samples is recognized as being statistically significant.
U.S. Pat. No. 4,855,897 METHOD AND APPARATUS FOR STATISTICAL SET POINT BIAS CONTROL describes a method by which sample values in the sample space are weighted by an exponential function of their age to reduce the significance of older samples on the calculated actual distribution of sample values. This patent also describes a method by which the controller is biased to respond more quickly to deviations on one side of the mean value than deviations on the other side of the mean value.